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Activity Number:
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335
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Memorial
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| Abstract - #300239 |
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Title:
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Nonparametric Monotone Regression for Generalized Linear Models with Applications to Wafer Acceptance Tests
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Author(s):
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Jyh-Jen H. Shiau*+
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Companies:
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National Chiao Tung University
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Address:
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Institute of Statistics, Hsinchu, Taiwan, 30050, Taiwan
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Keywords:
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Monotone nonparametric regression ; generalized linear model ; Bernoulli data ; natural cubic splines ; smoothing splines ; Wafer Acceptance Tests
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Abstract:
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Motivated by an engineering control problem of wafer acceptance tests (WAT) in semiconductor manufacturing, we develop a new nonparametric monotone smoothing-spline-based smoother for analyzing responses from exponential families. The new method modifies the monotone smoother developed by Zhang (2004) and then combines with the methodology developed by Gu (2002) for data from exponential families. An efficient algorithm is provided. A simulation study demonstrates that the proposed method performs well in the regression models with Bernoulli responses. In terms of the averaged squared error, the proposed monotone estimator outperforms the unconstrained smoother when the latter produces non-monotone estimates, while retaining about the same performance otherwise. It is demonstrated that the proposed method can be used in screening WAT test items for more stringent engineering control.
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